#!/bin/bash
  # Copyright 2012-2014  Johns Hopkins University (Author: Daniel Povey, Yenda Trmal)
  # Apache 2.0
  
  # This script is like steps/scoring/score_kaldi_wer.sh except it transcribes the <unk>'s
  # using local/unk_arc_post_to_transcription.py and also it calls
  # steps/scoring/score_kaldi_cer.sh at the end.
  
  [ -f ./path.sh ] && . ./path.sh
  
  # begin configuration section.
  cmd=run.pl
  stage=0
  decode_mbr=false
  stats=true
  beam=6
  word_ins_penalty=0.0,0.5,1.0
  min_lmwt=3
  max_lmwt=13
  iter=final
  #end configuration section.
  
  echo "$0 $@"  # Print the command line for logging
  [ -f ./path.sh ] && . ./path.sh
  . parse_options.sh || exit 1;
  
  if [ $# -ne 3 ]; then
    echo "Usage: $0 [--cmd (run.pl|queue.pl...)] <data-dir> <lang-dir|graph-dir> <decode-dir>"
    echo " Options:"
    echo "    --cmd (run.pl|queue.pl...)      # specify how to run the sub-processes."
    echo "    --stage (0|1|2)                 # start scoring script from part-way through."
    echo "    --decode_mbr (true/false)       # maximum bayes risk decoding (confusion network)."
    echo "    --min_lmwt <int>                # minumum LM-weight for lattice rescoring "
    echo "    --max_lmwt <int>                # maximum LM-weight for lattice rescoring "
    exit 1;
  fi
  
  data=$1
  lang_or_graph=$2
  dir=$3
  model_path=`echo $dir |xargs dirname`
  symtab=$lang_or_graph/words.txt
  
  for f in $symtab $dir/lat.1.gz $data/text; do
    [ ! -f $f ] && echo "score.sh: no such file $f" && exit 1;
  done
  
  
  ref_filtering_cmd="cat"
  [ -x local/wer_output_filter ] && ref_filtering_cmd="local/wer_output_filter"
  [ -x local/wer_ref_filter ] && ref_filtering_cmd="local/wer_ref_filter"
  hyp_filtering_cmd="cat"
  [ -x local/wer_output_filter ] && hyp_filtering_cmd="local/wer_output_filter"
  [ -x local/wer_hyp_filter ] && hyp_filtering_cmd="local/wer_hyp_filter"
  
  
  if $decode_mbr ; then
    echo "$0: scoring with MBR, word insertion penalty=$word_ins_penalty"
  else
    echo "$0: scoring with word insertion penalty=$word_ins_penalty"
  fi
  
  
  mkdir -p $dir/scoring_kaldi
  cat $data/text | $ref_filtering_cmd > $dir/scoring_kaldi/test_filt.txt || exit 1;
  if [ $stage -le 0 ]; then
  
    for wip in $(echo $word_ins_penalty | sed 's/,/ /g'); do
      mkdir -p $dir/scoring_kaldi/penalty_$wip/log
  
      if $decode_mbr ; then
        $cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring_kaldi/penalty_$wip/log/best_path.LMWT.log \
          acwt=\`perl -e \"print 1.0/LMWT\"\`\; \
          lattice-scale --inv-acoustic-scale=LMWT "ark:gunzip -c $dir/lat.*.gz|" ark:- \| \
          lattice-add-penalty --word-ins-penalty=$wip ark:- ark:- \| \
          lattice-prune --beam=$beam ark:- ark:- \| \
          lattice-mbr-decode  --word-symbol-table=$symtab \
          ark:- ark,t:- \| \
          utils/int2sym.pl -f 2- $symtab \| \
          $hyp_filtering_cmd '>' $dir/scoring_kaldi/penalty_$wip/LMWT.txt || exit 1;
  
      else
        $cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring_kaldi/penalty_$wip/log/best_path.LMWT.log \
          lattice-scale --inv-acoustic-scale=LMWT "ark:gunzip -c $dir/lat.*.gz|" ark:- \| \
          lattice-add-penalty --word-ins-penalty=$wip ark:- ark:- \| \
          lattice-1best ark:- ark:- \| \
          lattice-align-words $lang_or_graph/phones/word_boundary.int $model_path/final.mdl ark:- ark:- \| \
          lattice-arc-post $model_path/final.mdl ark:- - \| \
          local/unk_arc_post_to_transcription.py $lang_or_graph/phones.txt $lang_or_graph/words.txt data/lang_unk/oov.int \| \
          $hyp_filtering_cmd '>' $dir/scoring_kaldi/penalty_$wip/LMWT.txt || exit 1;
      fi
  
      $cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring_kaldi/penalty_$wip/log/score.LMWT.log \
        cat $dir/scoring_kaldi/penalty_$wip/LMWT.txt \| \
        compute-wer --text --mode=present \
        "ark:cat $dir/scoring_kaldi/test_filt.txt |" ark,p:- ">&" $dir/wer_LMWT_$wip || exit 1;
  
    done
  fi
  
  
  
  if [ $stage -le 1 ]; then
  
    for wip in $(echo $word_ins_penalty | sed 's/,/ /g'); do
      for lmwt in $(seq $min_lmwt $max_lmwt); do
        # adding /dev/null to the command list below forces grep to output the filename
        grep WER $dir/wer_${lmwt}_${wip} /dev/null
      done
    done | utils/best_wer.sh  >& $dir/scoring_kaldi/best_wer || exit 1
  
    best_wer_file=$(awk '{print $NF}' $dir/scoring_kaldi/best_wer)
    best_wip=$(echo $best_wer_file | awk -F_ '{print $NF}')
    best_lmwt=$(echo $best_wer_file | awk -F_ '{N=NF-1; print $N}')
  
    if [ -z "$best_lmwt" ]; then
      echo "$0: we could not get the details of the best WER from the file $dir/wer_*.  Probably something went wrong."
      exit 1;
    fi
  
    if $stats; then
      mkdir -p $dir/scoring_kaldi/wer_details
      echo $best_lmwt > $dir/scoring_kaldi/wer_details/lmwt # record best language model weight
      echo $best_wip > $dir/scoring_kaldi/wer_details/wip # record best word insertion penalty
  
      $cmd $dir/scoring_kaldi/log/stats1.log \
        cat $dir/scoring_kaldi/penalty_$best_wip/$best_lmwt.txt \| \
        align-text --special-symbol="'***'" ark:$dir/scoring_kaldi/test_filt.txt ark:- ark,t:- \|  \
        utils/scoring/wer_per_utt_details.pl --special-symbol "'***'" \| tee $dir/scoring_kaldi/wer_details/per_utt \|\
         utils/scoring/wer_per_spk_details.pl $data/utt2spk \> $dir/scoring_kaldi/wer_details/per_spk || exit 1;
  
      $cmd $dir/scoring_kaldi/log/stats2.log \
        cat $dir/scoring_kaldi/wer_details/per_utt \| \
        utils/scoring/wer_ops_details.pl --special-symbol "'***'" \| \
        sort -b -i -k 1,1 -k 4,4rn -k 2,2 -k 3,3 \> $dir/scoring_kaldi/wer_details/ops || exit 1;
  
      $cmd $dir/scoring_kaldi/log/wer_bootci.log \
        compute-wer-bootci --mode=present \
          ark:$dir/scoring_kaldi/test_filt.txt ark:$dir/scoring_kaldi/penalty_$best_wip/$best_lmwt.txt \
          '>' $dir/scoring_kaldi/wer_details/wer_bootci || exit 1;
  
    fi
  fi
  
  steps/scoring/score_kaldi_cer.sh --cmd "$cmd" --stage 2 --min-lmwt $min_lmwt \
                                   --max-lmwt $max_lmwt --word-ins-penalty $word_ins_penalty \
                                   $data $lang_or_graph $dir
  
  # If we got here, the scoring was successful.
  # As a  small aid to prevent confusion, we remove all wer_{?,??} files;
  # these originate from the previous version of the scoring files
  # i keep both statement here because it could lead to confusion about
  # the capabilities of the script (we don't do cer in the script)
  rm $dir/wer_{?,??} 2>/dev/null
  rm $dir/cer_{?,??} 2>/dev/null
  
  exit 0;